At home adaptive dual target deep brain stimulation in Parkinson's disease with proportional control.

Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus is an effective treatment for the motor symptoms of Parkinson's disease. The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. Continuous DBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and globus pallidus (dual target DBS) in a prospective within-participant, clinical trial in six patients with Parkinson's disease (n = 6, 55-65 years, n = 2 females). Dual target cDBS was tested for Parkinson's disease symptom control annually over 2 years, measured by motor rating scales, on time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral aDBS using a novel distributed (off-implant) architecture. In the home setting, we collected tremor and dyskinesia scores as well as individualized ß and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by Unified Parkinson's Disease Rating Scale (UPDRS) to a greater degree than either region alone (P < 0.05, linear mixed model) in the cohort. The amplitude of ß-oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (P < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artefacts and demonstrated a correlation between STN ß amplitude and DBS amplitude. Proportional plus integral control of aDBS reduced average power, while preserving UPDRS III scores in the clinic (P = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n = 3, P > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of Parkinson's disease over DBS of either the STN or globus pallidus alone. When combined with proportional plus integral aDBS, stimulation power may be reduced, while preserving the increased benefit of dual target DBS.

ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment.

Deep Brain Stimulation (DBS) stands as an effective intervention for alleviating the motor symptoms of Parkinson's disease (PD). Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i.e., continuous DBS (cDBS). However, they in general suffer from energy inefficiency and side effects, such as speech impairment. Recent research has focused on adaptive DBS (aDBS) to resolve the limitations of cDBS. Specifically, reinforcement learning (RL) based approaches have been developed to adapt the frequencies of the stimuli in order to achieve both energy efficiency and treatment efficacy. However, RL approaches in general require significant amount of training data and computational resources, making it intractable to integrate RL policies into real-time embedded systems as needed in aDBS. In contrast, contextual multi-armed bandits (CMAB) in general lead to better sample efficiency compared to RL. In this study, we propose a CMAB solution for aDBS. Specifically, we define the context as the signals capturing irregular neuronal firing activities in the BG regions (i.e., beta-band power spectral density), while each arm signifies the (discretized) pulse frequency of the stimulation. Moreover, an {\epsilon}-exploring strategy is introduced on top of the classic Thompson sampling method, leading to an algorithm called {\epsilon}-Neural Thompson sampling ({\epsilon}-NeuralTS), such that the learned CMAB policy can better balance exploration and exploitation of the BG environment. The {\epsilon}-NeuralTS algorithm is evaluated using a computation BG model that captures the neuronal activities in PD patients' brains. The results show that our method outperforms both existing cDBS methods and CMAB baselines.

Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment.

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) stimuli at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.

Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting.

Purpose: This study aims to meet a growing need for a fully automated, learning-based interpretation tool for retinal images obtained remotely (e.g. teleophthalmology) through different imaging modalities that may include imperfect (uninterpretable) images. Methods: A retrospective study of 1148 optical coherence tomography (OCT) and color fundus photography (CFP) retinal images obtained using Topcon's Maestro care unit on 647 patients with diabetes. To identify retinal pathology, a Convolutional Neural Network (CNN) with dual-modal inputs (i.e. CFP and OCT images) was developed. We developed a novel alternate gradient descent algorithm to train the CNN, which allows for the use of uninterpretable CFP/OCT images (i.e. ungradable images that do not contain sufficient image biomarkers for the reviewer to conclude absence or presence of retinal pathology). Specifically, a 9:1 ratio to split the training and testing dataset was used for training and validating the CNN. Paired CFP/OCT inputs (obtained from a single eye of a patient) were grouped as retinal pathology negative (RPN; 924 images) in the absence of retinal pathology in both imaging modalities, or if one of the imaging modalities was uninterpretable and the other without retinal pathology. If any imaging modality exhibited referable retinal pathology, the corresponding CFP/OCT inputs were deemed retinal pathology positive (RPP; 224 images) if any imaging modality exhibited referable retinal pathology. Results: Our approach achieved 88.60% (95% confidence interval [CI] = 82.76% to 94.43%) accuracy in identifying pathology, along with the false negative rate (FNR) of 12.28% (95% CI = 6.26% to 18.31%), recall (sensitivity) of 87.72% (95% CI = 81.69% to 93.74%), specificity of 89.47% (95% CI = 83.84% to 95.11%), and area under the curve of receiver operating characteristic (AUC-ROC) was 92.74% (95% CI = 87.71% to 97.76%). Conclusions: Our model can be successfully deployed in clinical practice to facilitate automated remote retinal pathology identification. Translational Relevance: A fully automated tool for early diagnosis of retinal pathology might allow for earlier treatment and improved visual outcomes.

Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks.

Purpose: To develop a neural network (NN)-based approach, with limited training resources, that identifies and counts the number of retinal pigment epithelium (RPE) cells in confocal microscopy images obtained from cell culture or mice RPE/choroid flat-mounts. Methods: Training and testing dataset contained two image types: wild-type mice RPE/choroid flat-mounts and ARPE 19 cells, stained for Rhodamine-phalloidin, and imaged with confocal microscopy. After image preprocessing for denoising and contrast adjustment, scale-invariant feature transform descriptors were used for feature extraction. Training labels were derived from cells in the original training images, annotated and converted to Gaussian density maps. NNs were trained using the set of training input features, such that the obtained NN models accurately predicted corresponding Gaussian density maps and thus accurately identifies/counts the cells in any such image. Results: Training and testing datasets contained 229 images from ARPE19 and 85 images from RPE/choroid flat-mounts. Within two data sets, 30% and 10% of the images, were selected for validation. We achieved 96.48% ± 6.56% and 96.88% ± 3.68% accuracy (95% CI), on ARPE19 and RPE/choroid flat-mounts. Conclusions: We developed an NN-based approach that can accurately estimate the number of RPE cells contained in confocal images. Our method achieved high accuracy with limited training images, proved that it can be effectively used on images with unclear and curvy boundaries, and outperformed existing relevant methods by decreasing prediction error and variance. Translational Relevance: This approach allows efficient and effective characterization of RPE pathology and furthermore allows the assessment of novel therapeutics.